Instructions to use ayh015/myLightningOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayh015/myLightningOPD with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ayh015/myLightningOPD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayh015/myLightningOPD")
model = AutoModelForCausalLM.from_pretrained("ayh015/myLightningOPD", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ayh015/myLightningOPD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayh015/myLightningOPD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayh015/myLightningOPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayh015/myLightningOPD

SGLang

How to use ayh015/myLightningOPD with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ayh015/myLightningOPD" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayh015/myLightningOPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ayh015/myLightningOPD" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayh015/myLightningOPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayh015/myLightningOPD with Docker Model Runner:
```
docker model run hf.co/ayh015/myLightningOPD
```

myLightningOPD

File size: 2,198 Bytes

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#!/usr/bin/env bash
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

# Step 1: Generate SFT data using the teacher model.
#
# Uses data_curation/ to run the teacher model on OpenThoughts-3 prompts
# and generate response trajectories for SFT training.
#
# Required environment variables:
#   TEACHER_MODEL - HuggingFace model name or path (e.g. Qwen/Qwen3-8B)
#   SFT_PROMPTS   - Path to the prompt dataset (.jsonl or .parquet)
#   OUTPUT_DIR    - Directory for generated SFT data
#
# Optional:
#   NUM_GPUS      - Number of GPUs to use (default: 8)
#   TP_SIZE       - Tensor parallel size per worker (default: 1)
#
# Extra args are passed through to data_curation/pipeline.py, e.g.:
#   bash scripts/generate_sft_data.sh --num-samples 10

set -euo pipefail

# ---- CUDA / FlashInfer build environment ----
if [ -n "${CONDA_PREFIX:-}" ]; then
    export CUDA_HOME="${CUDA_HOME:-$CONDA_PREFIX}"
    export CUDA_PATH="${CUDA_PATH:-$CONDA_PREFIX}"
    export CUDACXX="${CUDACXX:-$CONDA_PREFIX/bin/nvcc}"

    export PATH="$CONDA_PREFIX/bin:$PATH"
    export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$CONDA_PREFIX/lib64:/usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH:-}"
    export LIBRARY_PATH="/usr/lib/x86_64-linux-gnu:${LIBRARY_PATH:-}"
fi

echo "Using nvcc: $(which nvcc)"
nvcc --version || true
echo "CUDA_HOME=${CUDA_HOME:-}"
echo "CUDACXX=${CUDACXX:-}"
echo "LIBRARY_PATH=${LIBRARY_PATH:-}"
# ---------------------------------------------

: "${TEACHER_MODEL:?Set TEACHER_MODEL (e.g. Qwen/Qwen3-8B)}"
: "${SFT_PROMPTS:?Set SFT_PROMPTS to the prompt dataset path}"
: "${OUTPUT_DIR:?Set OUTPUT_DIR for generated SFT data}"

# Resolve to absolute paths (workers may run from different cwd)
SFT_PROMPTS="$(cd "$(dirname "${SFT_PROMPTS}")" && pwd)/$(basename "${SFT_PROMPTS}")"
OUTPUT_DIR="$(mkdir -p "${OUTPUT_DIR}" && cd "${OUTPUT_DIR}" && pwd)"

NUM_GPUS="${NUM_GPUS:-8}"
TP_SIZE="${TP_SIZE:-1}"

bash data_curation/run_curation.sh \
    --model "${TEACHER_MODEL}" \
    --input "${SFT_PROMPTS}" \
    --output-dir "${OUTPUT_DIR}" \
    --num-gpus "${NUM_GPUS}" \
    --tensor-parallel-size "${TP_SIZE}" \
    "$@"